Surface-Enhanced Raman Scattering on Dendrimer/Metallic Nanoparticle Layer-by-Layer Film Substrates
Bibliographic record
Abstract
In this paper, the fabrication, characterization, and application of unique layer-by-layer (LBL) films of dendrimers and metallic nanoparticles is reported. Silver nanoparticles (d = approximately 20 nm) are produced in solution by sodium citrate reduction and incorporated into thin films with generation 1 and 5 DAB-Am dendrimers (polypropylenimine dendrimers with amino surface groups) by the LBL technique. The resulting nanocomposite films are characterized by UV-visible surface plasmon absorption and atomic force microscopy (AFM) measurements, and employed as substrates for surface-enhanced Raman scattering (SERS) of 2-naphthalenethiol. Through variation of the molecular size (dendrimer generation) and concentration of the cross-linker used, as well as the number of layers produced, the optical properties of several different possible architectures are studied. In the films, Ag nanoparticles are shown to be effectively immobilized and stabilized with increased control over their spacing and aggregation. Moreover, the films are shown to be excellent substrates for SERS measurements, demonstrating significant enhancement capability. As expected, large electromagnetic enhancement of Raman scattering signals is found to be strongly dependent on interparticle coupling between neighboring metallic nanoparticles. Finally, the possibility of detecting SERS signals from architectures with intervening layers between the metal nanoparticles and analyte molecules is explored. It is shown that although there are decreases in intensity with increasing number of intervening layers (as is expected from the distance dependence of SERS), electromagnetic enhancement is still able to function at these distances, thus offering the possibility of developing sensors with external layers that are chemically selective for specific analytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".